MARINE ECOLOGY PROGRESS SERIES
Mar Ecol Prog Ser
Vol. 364: 135–146, 2008
doi: 10.3354/meps07471
Published July 28
Stable trophic structure across coastal nekton
assemblages despite high species turnover
Sébastien Villéger1,*, Julia Ramos Miranda2, Domingo Flores Hernandez 2,
Atahualpa Sosa Lopez 2, David Mouillot1
1
UMR CNRS-IFREMER-UM2 5119 Écosystèmes Lagunaires, Université Montpellier 2, CC 093, Montpellier 34095 Cedex 5,
France
2
Centro de Ecología, Pesquerías y Oceanografía de Golfo de México (EPOMEX), Universidad Autónoma de Campeche,
Av. Agustín Melgar s/n, Campeche 24030, Mexico
ABSTRACT: The finding of invariant structures in species assemblages is of primary importance for
ecology because it would suggest that, despite species turnover and environmental gradients, some
self-organizing principles may shape these assemblages. Tropical estuarine and coastal ecosystems
are ideal for investigating patterns in trophic structures because they contain many species and are
characterized by a high variability for both biotic and abiotic variables. We used the data from a
150 km long transect in the Terminos Lagoon region (Campeche State, Mexico) where 37 stations
were sampled monthly during 1 yr for both abiotic parameters and nektonic assemblages. We then
quantified 3 complementary components of trophic diversity (trophic richness, trophic evenness and
trophic divergence) and then challenged the idea that some stable structures may emerge. We found
that abiotic parameters, space and time have weak explanatory power on trophic diversity indices.
We also observed a high species turnover both at local and regional scales, but it was unrelated to the
small variations of trophic diversity indices. This stability of trophic structure is partly due to the predominance of the trophic class 3.25 to 3.5, which accounted invariably for between 50 and 60% of the
total nekton biomass across space and time. These findings suggest that the species turnover
observed in our system is not random but, rather, allows maintenance of the same abundance distribution along the trophic axis. The mechanisms underlying these emergent properties of trophic
structures deserve to be investigated through the use of trophodynamic models.
KEY WORDS: Trophic diversity · Trophic level · Environmental gradients · Terminos Lagoon · Gulf
of Mexico
Resale or republication not permitted without written consent of the publisher
Despite the extraordinary complexity of natural systems, some ecologists expect that a few relatively simple laws may govern patterns such as the structure of
species assemblages (Hubbell 2001). Uncovering general and repeatable patterns in species assemblages
over space and time has been a major focus of research
in ecology because identification of such patterns
would suggest that apparently diverse and idiosyncratic assemblages may have common principles. Ultimately, the goal of such a comparative approach
should be to identify the processes underpinning any
observed universal pattern, which in turn would provide crucial knowledge allowing advances in conservation and environmental management (Mouillot
2007).
Trophic ecology has become a fertile area for discussion of intriguing regularities in assemblages of interacting species feeding on each other. After many contradictory investigations about the scale-invariance of
trophic chain length, predator/prey ratio and connectance (relative density of links) in food webs
(Briand & Cohen 1984, Sugihara et al. 1989, Havens
*Email: [email protected]
© Inter-Research 2008 · www.int-res.com
INTRODUCTION
136
Mar Ecol Prog Ser 364: 135–146, 2008
1992, Martinez 1992), there has been an emerging
consensus about the scale dependence of these trophic
structures (Martinez 1994, Deb 1995, Bersier & Sugihara 1997, Bersier et al. 1999). However, the repeatability or the stability of trophic structures over space
and time at the same scale of observation is still questionable. Moreover, the trophic structure of an assemblage cannot be fully captured by a single number
(such as the connectance or the trophic chain length),
and many facets of this structure, including species
abundances and the distribution of biomass along
trophic levels, have been overlooked due to a lack of
adequate descriptors. Thus, the knowledge of the
trophic structure of ecological assemblages is still
incomplete, and the spatiotemporal stability in these
structures remains unexplored. In the present study,
using complementary trophic diversity indices, we
sought ubiquity in the trophic structure of nekton
assemblages in the Terminos Lagoon region (southern
Gulf of Mexico) despite high environmental and species turnover.
Coastal lagoon ecosystems are of primary concern
for human population welfare because they provide
various services of high value (protein source, regulation of pollution and recreational areas), while anthropogenic activities severely impact them (Costanza et
al. 1997). Fluctuating environmental conditions on
short spatial and temporal scales also mark these
ecosystems. This fluctuation is the consequence of low
inertia to external events due to shallowness of water
masses coupled with an interface position between
marine and freshwater bodies (Basset et al. 2006).
These ecological systems provide unique opportunities
to investigate invariant patterns in the structure of
assemblages because the high variability in environmental conditions generates a high species turnover
across space and time (e.g. Wagner 1999). In these
coastal systems, the nekton, including shrimps, fish,
crabs and squids, is a major biological component.
These macroorganisms have a large range of life-history traits, migratory behaviors and diets, thus occupying a key position in the flux of matter through space
and time (e.g. Holmlund & Hammer 1999). Identifying
regularities in the structure of nekton assemblages in
such ecosystems could be challenging because it
would suggest the predominance of trophic-based processes despite high turnover in species identity and
environmental conditions.
The vertical diversity of the food web is a component
of biodiversity and plays a role in the productivity and
stability of ecosystems (Diaz et al. 2006, Duffy et al.
2007). However, no consensus has emerged about correct estimations of an assemblage’s vertical food web
diversity, which we designate as ‘trophic diversity.’
Similarly to functional diversity, trophic diversity can-
not be summarized using a single number. Therefore,
we chose to estimate various facets of nekton assemblage trophic diversity using the framework of Mason
et al. (2005), who defined 3 primary components of
diversity (richness, evenness and divergence) with corresponding indices. These indices were based on the
trophic level index which is an integrative parameter
based on food items and diet composition and thus
characterizes the position of species along the trophic
chain. Trophic level is available for a great number of
nektonic species across taxa, allowing inclusion of all
species in studies of taxonomically and functionally
heterogeneous assemblages. In addition, trophic level
is a key parameter for modeling marine ecosystem
functioning (Pauly et al. 2000, Gascuel 2005) or for
studying fishery dynamics (Pauly et al. 1998, Pauly &
Watson 2005). For instance, the mean trophic level of
fishery landings (MTI, Marine Trophic Index) was a
relevant indicator demonstrating that we have been
‘fishing down marine food webs’ (Pauly et al. 1998).
However, the mean trophic level refers only to the central position in the distribution of biomass along the
trophic chain; in the study of Pauly et al. (1998), the
variability of the biomass distribution along this trophic
chain was not included. Therefore, the purpose of the
present study is to quantify biomass distribution along
the trophic axis by introducing a new set of trophic
diversity indices: trophic richness, trophic evenness
and trophic divergence.
As an application, we estimated these indices for the
nekton assemblages of the Terminos Lagoon region
and investigated their variations. In particular, we analyzed how trophic diversity varies with environmental
conditions and through space and time. Then we
tested the ubiquity of the trophic structure in relation
to species turnover and at 2 scales of observation (local
to regional).
MATERIALS AND METHODS
Study area. The study area is located along the
southern coast of the Campeche State (Mexico)
(Fig. 1). The Terminos Lagoon, which is 70 km long
and 30 km wide, highly influences this part of the Gulf
of Mexico. This wetland ecosystem is of ecological
interest for flora and fauna and for terrestrial and
aquatic biota. Several anthropogenic factors have
affected it in past decades, including the development
of intensive farming with concurrent deforestation of
mangroves, intensive shrimp fishing, offshore oil extraction and the urbanization of Carmen Island (Ramos
Miranda et al. 2005a). Moreover, previous studies have
identified a shift from hypohaline to euhaline/hyperhaline conditions in the lagoon (salinity has been
Villéger et al.: Stable trophic structure in nekton
137
Trophic diversity indices. Functional diversity could be seen as the distribution of
functional trait values and of abundance
among entities in a given space, entities
being either species at the community level
or individuals at the population level. In
the present study, we focused on the nekton trophic diversity by considering the
trophic level as a functional trait. Following
Grime (1998), who proposed the biomass
ratio effect, we chose biomass as the most
relevant measure for abundance; therefore, trophic diversity could be seen as the
Fig. 1. Study area and 37 sampled stations. Symbols indicating each station
distribution of biomass along the trophic
depend on the spatial group to which the station belongs (see Fig. 3): (J)
level axis (Fig. 2). As with species biodiCoast 1, (M) Coast 2 and (d) Lagoon
versity, which can be split into different
facets (e.g. species richness and evenness,
Purvis & Hector 2000), functional diversity was split into
increasing from 24.67 PSU in the 1980s to 26.8 PSU at
3 complementary components (Mason et al. 2005):
the end of the 1990s) combined with a decreasing
richness, evenness and divergence. We applied these 3
water depth (Ramos-Miranda et al. 2005b, Sosa-López
components to the distribution of abundance along a
et al. 2005).
trophic axis to obtain a measure of trophic richness,
The present study focuses on a 150 km long trantrophic evenness and trophic divergence.
sect (18° 37’ 16” N, 92° 42’ 28” W to 18° 30’ 20” N,
Trophic richness represents the proportion of the
91°28’03” W) including 37 stations distributed in the
trophic chain an assemblage fills. It could be estimated
southwestern part of the Terminos Lagoon and along
by either the number of trophic levels (trophic richness
the adjacent coast (Fig. 1). This transect crosses the
sensu stricto) or the maximum range of trophic levels
discharge of 3 main rivers (the Usumacinta, San Pedro
(length of the trophic chain) present in a nekton assemy San Pablo and Palizada rivers) and the Carmen inlet,
blage (Fig. 2). These 2 indices are complementary and
the exit of the Terminos Lagoon flow (David & Kjerfve
potentially unrelated. For instance, the range can be
1998).
high with the presence of only 2 extreme trophic levels
Sampling protocol. We conducted a monthly biolog(1 at each side of the trophic chain), whereas an assemical survey of the 37 stations from February 2003 to
blage with many species having close trophic levels
January 2004 (Fig. 1), locating sampling points using a
will show a relatively small range.
Global Positioning System with a precision of 75 m.
Trophic evenness describes how equitably distribThe survey included both abiotic and biotic parameuted the biomass is along the trophic axis. It includes
ters. We recorded environmental variables such as waboth the regularity of the distribution of values along
ter temperature, salinity, pH and dissolved oxygen for
the trophic axis and the evenness of the abundance
the top and bottom of the water column using Hydroamong these trophic levels. The functional regularity
lab HL 2011 equipment. Additionally, we measured
index (FRO) proposed by Mouillot et al. (2005) includes
depth and water transparency using a Secchi disk.
these 2 aspects. In the present study, we used a stanNektonic assemblages were sampled using a shrimp
dardized version of the proposed index: for an assemtrawl (length: 5 m, mouth opening diameter: 2.5 m,
blage of S species, TL and corresponding biomass (w)
mesh size: 19 mm) towed 12 min at a constant speed of
were ranked increasingly to compute S –1 weighted
2.5 knots. The volume sampled was thus 4500 m3. Indievenness differences (EW) for species i and j using:
vidual organisms were kept in ice on the boat before
being frozen. For each sample, all individuals were
TLi − TL j
EWl =
(1)
identified to the species level using the keys of Fischer
w i +w j
(1978), Castro-Aguirre (1978) and Resendez Medina
where l represents the S –1 pairs of species. These val(1981a,b), and weighed to the nearest dg.
ues were then standardized to a percentage weighted
For each fish species, the trophic level index value
evenness:
(TL) was recorded according to FishBase (Froese &
Pauly 2006). For invertebrates (shrimps, crabs, mantis
EW
PEWl = S −1 l
shrimp and squids), we used the trophic level recorded
(2)
in FishBase (Froese & Pauly 2000) at the best taxoEW
∑ l
l =1
nomic level available.
138
Mar Ecol Prog Ser 364: 135–146, 2008
the mean trophic level value, trophic
divergence would be low. Conversely,
trophic divergence would be high if biomass density peaks at the extremities of
the trophic axis. We estimated trophic
divergence with the FDvar index of
Mason et al. (2003). FDvar is based on an
abundance-weighted sum of squares
analogous to a log-transformed variance:
FDvar =
with
V =
2
arctan(5V ),
Π
S
∑w i × (lnTLi − lnTL)
(4)
i =1
where lnTL =
S
∑w i × lnTLi
i =1
By design, FDvar is independent of
species richness and constrained to the 0
to 1 range.
Statistical properties of the indices
were
already investigated in previous
Fig. 2. Theoretical presentation of trophic diversity components. From a hypothetical assemblage, an increase of each trophic diversity component is illuspapers (Mason et al. 2003, Mouillot et al.
trated in 3 directions: (a) increase in trophic richness with either an increase in
2005). These 4 trophic diversity indices
the number of entities (left) or an increase in the range of trophic level values
were computed using R software (R
(right); (b) increase in trophic divergence due to a shift of biomass from the
Development Core Team 2007).
middle of the trophic axis to extremities; or (c) an increase in trophic evenness
with either an increase in the regularity of the distribution of the trophic level
Statistical analysis. Sampling points
values along the axis (left) or an increase in the regularity of biomass among
with fewer than 3 trophic levels in the
these trophic level values (right)
assemblage were removed from statistical analysis. Indeed, with only 1 trophic
In the case of perfect regularity of abundance distrilevel, trophic range, trophic evenness and trophic
bution along the trophic axis, all EWl would be equal
divergence do not make sense. For samples with only 2
and all PEWl values would be (S –1)–1. Conversely,
species, (S –1)–1 is equal to 1, so the denominator of the
when PEWl values differ, trophic evenness must
FRO is 0 and the index is not defined. For assemblages
decrease. To this aim we compared PEWl values to
with more than 2 species, but with only 2 trophic levels
(i.e. some species have the same value of trophic level),
(S –1)–1 to obtain the FRO index:
there is only 1 PEW different from 0 and it is equal to 1
S −1
⎛ PEW , 1 ⎞ − 1
so that:
min
∑ ⎝
l
S − 1⎠ S − 1
FRO = l =1
S −1
(3)
1
1
1
(5)
∑ min ⎛⎝ PEWl , S − 1 ⎞⎠ = S − 1
1−
S −1
l=1
This index is designed to be independent of the
trophic richness and is constrained between 0 and 1.
The value 1 is obtained when all PEWl are equal to
(S –1)–1. Trophic evenness of a community would be
high if coexisting species have regularly spaced
trophic levels with similar biomass (Fig. 2).
Trophic divergence quantifies the divergence of the
nekton biomass distribution from the mean trophic
level of the assemblage (Fig. 2). It is therefore correlated with niche differentiation and may indicate the
potential resource use spectrum and intensity of competition. For example, with a maximum biomass near
and FRO is always 0.
The relationship between each of the 4 trophic diversity indices and abiotic parameters was estimated
using a Bayesian model selection procedure for multiple linear regressions (function ‘bicreg’ of the BMA
package under R software). This method is a Bayesianbased approach that quantifies the relative support of
various models in the data (Johnson & Omland 2004).
The selection procedure was conducted using the BIC
(Bayesian information criterion), and the inclusion of
models in the final set was based on the analysis of
Bayes factor (ratio of a model’s posterior probabilities
Villéger et al.: Stable trophic structure in nekton
interpreted as the likelihood of one model versus
another given the observed dataset; Burnham &
Anderson 2002).
A hierarchical classification using the Ward method
(R package cluster) was carried out to classify the 37
stations into zones according to the values of 10 environmental parameters for the 12 mo of the present
study. According to previous studies (Ramos Miranda
et al. 2005a), 3 seasons were defined based on weather
conditions (wind, rain and temperature): the dry
season (February to May), the wet season (June to
September) and the ‘Nortes’ season (October to
January).
Analyses of variance (ANOVA) with 2 factors (zone,
season and their interaction) were computed using the
4 trophic diversity indices (Table 1) and species richness as predicted variables.
For each pair of stations, we estimated dissimilarity
between the 2 assemblages by using the Bray-Curtis
index. We chose this index because it measures the 2
facets of biotic dissimilarity: species identity and species dominance.
Similarly for each pair of stations (i, j) and for each
trophic diversity index and species richness (Indk ), we
computed the distance dIndk (i, j) to estimate the
absolute relative difference in index values with:
dIndk (i, j) =
[ 2 × Indk (i) − Indk (j) ]
[ Indk (i)+ Indk ( j)]
(6)
Then, to test whether the biotic dissimilarity between 2 assemblages correlates with the dissimilarity
of their trophic diversity, 6 Mantel’s tests were computed (package ‘ape’ of R software) between the BrayCurtis dissimilarity matrix and each of the matrices
containing a diversity dissimilarity (richness and 4
trophic diversity indices).
Given the geographical zones discriminated by the
Ward classification and the a priori fixed seasons, we
aggregated data for species abundance from corresponding stations into spatiotemporal strata. Species
richness and trophic diversity indices were also estimated for each stratum. We then applied the same
methodology as at the local scale to discover whether
the dissimilarity in trophic diversity indices is related to
the dissimilarity in species composition.
RESULTS
Among the 444 sampling points (37 stations × 12 mo),
132 were removed before statistical analysis because
(1) environmental parameters were missing after technical problems (96 sampling points), or (2) the net was
empty (11 sampling points), (3) we caught fewer than 3
different trophic levels which is the minimum needed
139
to compute trophic diversity indices (28 sampling
points).
In the 312 remaining samples, a total of 36 744 nektonic organisms were caught for a total biomass of
~600 kg.
Total species richness was of 101 species: 83 teleosts,
5 elasmobranchs, 7 shrimp species (family Penaeidae),
4 crab species (genus Callinectes), 1 Mantis shrimp
(Squilla empusa) and 1 squid (Lolliguncula brevis).
Invertebrates (mainly shrimps and crabs) were often
an important part of the biomass (mean ± SD: 32.9 ±
27.6%). Their contribution to the total biomass of each
nekton assemblage ranged from 0 to 100%.
Environmental parameters
As expected, environmental variables showed large
ranges of values. Water depth varied from 0.8 to 12.4 m
with a mean ± SD of 4.2 ± 2.5 m. Transparency values
filled all the potential range (varying from 2 to 100%),
but the mean remained low (15.7 ± 14.7%). Similarly,
surface salinity ranged from 0.2 to 40.5 PSU (mean 28 ±
9.7 ppm) showing a gradient from euhaline to freshwater conditions. Water temperature was high (27.8 ±
2.6°C) with extreme values of 22.5 and 32.5°C during
the Nortes season and at the end of the dry season, respectively. The pH showed a large range of values from
5.05 to 8.94 (7.74 ± 0.72). Dissolved oxygen values
varied from 1.5 to 9.8 mg l–1 with a mean value of 6.4 ±
1.5 mg l–1.
As expected, given the low depth, physico-chemical
parameters measured at the surface and at the bottom
of the water column were highly correlated (0.949 for
temperature, 0.860 for salinity, 0.966 for pH and 0.834
for dissolved oxygen). Furthermore, some environmental parameters were significantly correlated with
one another, but Pearson coefficients of correlation
remained low (< 0.57). For example, depth and salinity
were positively correlated (r = 0.509 for surface water
and r = 0.555 for bottom waters). Temperature and dissolved oxygen were globally negatively linked with a
coefficient of correlation ranging from –0.466 to –0.569
for surface and bottom waters, respectively.
The hierarchical classification, based on environmental conditions, discriminated 3 main clusters
(Fig. 3): one group (Coast 1) with Stns 1 to 12 corresponding to the coast between the mouth of the
Usumacinta River and the mouth of the San Pablo y Pedro River (Fig. 1); a second group (Coast 2) with Stns 13
to 25 corresponding to the external edge of the Terminos lagoon; and a third group (Lagoon) with Stns 26 to
37 located in the southern part of the lagoon. In other
words this classification based on environmental parameters matches the geography of the coast (Fig. 1).
140
Mar Ecol Prog Ser 364: 135–146, 2008
1
2
4
6
3
5
7
9
11
8
10
12
13
15
17
19
24
25
14
16
20
23
21
22
18
26
27
28
32
33
34
35
29
30
31
36
37
Only 3 out of 6 variables had an effect on the 4
indices: depth, transparency and bottom temperature.
Furthermore, for each index, few environmental variables explained trophic diversity index variations.
Water depth negatively influenced the number of
trophic levels. The trophic range and the number of
trophic levels were positively affected by the bottom
temperature. Trophic divergence index was positively
related to water depth and transparency. Only water
depth influenced trophic evenness.
Overall, the linear models explained only between
3.8 and 15.7% of the total variation of nekton trophic
diversity indices.
Coast 1
Coast 2
Lagoon
Fig. 3. Dendrogram of the 37 stations according to a Ward’s classification based on 10 environmental parameters measured
each month. Three zones corresponding to the 3 main clusters
were named based on their position on the transect (Fig. 1)
Environmental effects on trophic diversity indices
Given the very strong correlations between physicochemical measures at the 2 layers (surface and bottom)
of the water column, we used only bottom temperatures, salinities, pH and dissolved oxygen values for
multiple regressions. Table 1 shows the results of the
Bayesian model selection procedure carried out for the
modeling of the 4 trophic diversity indices as a function
of depth, transparency, bottom temperatures, salinities, pH and dissolved oxygen values. Very similar
results were obtained using surface physico-chemical
variables, but we do not present them for reasons of
clarity. For each of the 4 indices, the evidence of the
best model over the second-best (given by the Bayes
factor) was always substantial, thus only 1 model was
retained during the selection.
Testing spatial and temporal effects on trophic
diversity
Table 2 shows the results of the ANOVAs carried out
at the local scale (station). Spatial effect significantly
influenced species richness and the number of trophic
levels, with more species collected in stations within
the lagoon than in stations situated outside. FDvar varied significantly with the zone while the interaction between season and zone factors was significant. The 2
other indices (range and FRO) were unaffected by the
2 factors or by their interaction. Fig. 4 clearly shows the
spatiotemporal consistency for these latter indices.
Relations between species turnover and trophic
diversity dissimilarities
At the local scale (station), the mean ± SD BrayCurtis dissimilarity index was 0.853 ± 0.147 between
samples.
The Mantel tests showed positive significant correlations between Bray-Curtis dissimilarities and each of
the 5 absolute relative differences for
indices but with low Mantel correlaTable 1. Results of a Bayesian model selection procedure for linear model
tion coefficients (0.21 for species richregressions carried out using trophic diversity indices as explained variables
ness and < 0.22 for the 4 trophic diverand 6 environmental parameters as explanatory variables for 312 sampling
points. Only the variables retained by the models are presented. For each
sity indices). This outcome means that
trophic diversity index, only one model was retained by the selection procedure.
even if the similarity between 2 comFor this model, the coefficient posterior expected values for the explanatory
munities significantly affects the simi2
variables, the R and Bayes factor (evidence of the best selected model versus
larity of their trophic structure, the
the second-best) are presented. FDvar = FDvar index of Mason et al. (2003);
FRO = functional regularity index. –: variables not selected in the final model
effect remains weak.
With the 3 seasons and 3 geographical zones, 9 spatiotemporal strata were
Bayes
Depth
TransBottom
R2
parency
temp.
constructed from the 312 stations. At
this regional scale the Bray-Curtis disNo. of trophic levels
–0.412
–
0.376
0.145
7.29
similarity among strata dropped to
Range of trophic levels
–
–
0.033
0.038
1.48
0.493 ± 0.101. For all trophic diversity
0.007
0.080
–
0.157
3.83
FDvar
indices, the Mantel tests showed no
FRO
0.012
–
–
0.039
5.76
significant relationships between the
141
Villéger et al.: Stable trophic structure in nekton
ronmental parameters drive the trophic structure
of nekton assemblages. Trophic richness is mainly
influenced by depth and temperature. Divergence
is affected by depth and transparency and evenness just by depth. However, and this is the key
point, the overall weak predictive power of the
multiple regressions (3.8 to 15.7%) suggests that
Index
Season
Zone
Zone × Season
the trophic diversity of nekton assemblages is rel(df = 2)
(df = 2)
(df = 4)
atively stable along environmental gradients.
No. of species
0.996ns
6.348**
2.147ns
This result is confirmed by ANOVA tests showns
ns
No. of trophic levels
2.501
10.585***
2.049
ing
that trophic range and trophic evenness are
Range of trophic levels
1.394ns
1.936ns
1.287ns
ns
invariant
across space and time (Fig. 4), while speFDvar
1.612
3.437*
2.949**
FRO
1.951ns
0.028ns
0.415ns
cies richness and the number of trophic levels differ among zones. The findings suggest that even
when changes occur in nektonic species richness,
trophic diversity indices remain stable over space and
indices and the species turnover in nekton species astime. More generally, Bray-Curtis dissimilarities besemblages. As an illustration, Fig. 5 presents the trotween pairs of samples demonstrate the high turnover
phic spectrum of the 9 spatiotemporal strata (regional
in the composition of nekton assemblages. The signifiscale), showing a global consistency for all indices
cant but weak correlations observed at the local scale
despite species turnover, particularly between stations
between these assemblage dissimilarities and differbelonging to the wet and Nortes seasons.
ences observed for trophic diversity indices do not
Table 2. ANOVA F-values for the effect of spatial and temporal factors on 4 trophic diversity indices and species richness for 312 sampling points. The degree of freedom (df) of each factor is given at
the head of the column. FDvar = FDvar index of Mason et al. (2003);
FRO = functional regularity index. nsnon significant; *p < 0.05;
**p < 0.01; ***p < 0.001
DISCUSSION
0.08
FDvar
10
5
0.06
0.04
0.02
0
0.00
15
0.5
0.4
10
FRO
No. of trophic levels
No. of species
15
5
0.3
0.2
0.1
0
0.0
Coast 1 Coast 2 Lagoon
2.0
Range of
trophic levels
Environmental conditions were highly variable in space and time among our sampling
points, as expected from the freshwater influence in this coastal area. Indeed, environmental
conditions range from freshwater (near the
mouth of the streams) to marine water (near the
northern part of Carmen Island) with many
intermediate states in terms of salinity, transparency and water depth (from the inner lagoon
to the adjacent coast). More generally, the
whole area could be divided into 3 main zones
according to environmental conditions, and
these 3 zones are consistent with the global
hydrology (influence of the streams, current
flowing thought the lagoon and marine water
entries) (Figs. 1 & 3). In parallel, the nekton
composition in terms of species abundances is
also highly different among samples (Bray-Curtis ranging from 0.082 to 1) and among spatiotemporal strata (Bray-Curtis ranging from
0.234 to 0.666), highlighting the high species
turnover in these estuarine environments (e.g.
Wagner 1999). The present case study is thus
ideal to seek potentially stable patterns in
trophic structure because tropical estuarine
ecosystems contain many species and are characterized by high variability for both biotic and
abiotic variables.
The multiple regressions with environmental
variables (Table 1) showed that only a few envi-
1.5
1.0
0.5
0.0
Coast 1 Coast 2
Lagoon
Fig. 4. Mean (± SE) of species richness and of 4 trophic diversity
indices estimated over areas (see Figs. 1 & 3) and seasons. ‘Dry’
(white bars) corresponds to the period from February to May, ‘wet’
(grey bars) to June to September and ‘Nortes’ (black bars) to October
to January. ANOVA tests are summarized in Table 2. FDvar = FDvar
index of Mason et al. (2003); FRO = functional regularity index
142
Mar Ecol Prog Ser 364: 135–146, 2008
Coast 1
% of biomass
Coast 2
Coast 3
Dry
60
50
40
30
20
10
0
60
50
40
30
20
10
0
4
Xk
3 0
Ca
4
Xk
Ca
4
0
Wet
Range = 2.34
FDvar = 0.056
8
FRO = 0.333
Cm
9
4 8 Af Sl 3 3 2
13
Range = 2.39
FDvar = 0.063
FRO = 0.288
7
9
3 St Cm Sl 3 4 3
60
Range = 2.39
50
15 FDvar = 0.06
FRO = 0.319
40
Ds
12
30
20
Cm
10 3
3 4 Bc
11 3
4 3
Ar 1
0
2 2.5 3
3.5 4
4.5
60
50
40
30
20
10
0
60
50
40
30
20
10
0
Nortes
10 Range = 2.34
FDvar = 0.036
Ci
FRO = 0.332
Cm 14
4
8
3 0 Ca 5
Sl 3 5 3
Range = 2.39
11 FD = 0.05
var
FRO = 0.342
Ci
5
4
0 Ca
12
5
Cm 14
Af Sl 1 5 3
19
60
Range = 2.5
FDvar = 0.061
50
FRO = 0.371
40
30
Cm
20
12
5
10
5
15 4 5
4 Bc
Ar 1
4
0
2 2.5 3
3.5 4
4.5
12 Range = 2.34
60
50
FDvar = 0.064
FRO = 0.34
40
30
Cm
20
14
5
10 4
Sl 3 7 3
Se 0 Xk 3 6
0
60
50
40
30
20
10
0
60
50
40
30
20
10
0
12 Range = 2.39
FDvar = 0.045
Ci FRO = 0.32
3
10 Cm 12
4 1 Xk 4 St Bm Sl 4 6 3
17
Range = 2.39
FDvar = 0.044
Cf FRO = 0.41
3
2
11 Cm
5
1
2.5
14
2 4 3
4
3
3.5
4
4.5
Trophic level
Fig. 5. Biomass distribution along the trophic level (TL) axis for each of the 9 spatiotemporal strata. Ten classes of TL were
considered (each class has a width of 0.25). The values of the 3 trophic diversity indices are given for each stratum. For each
class, the number of species is given at the top of the corresponding bar. The relative biomasses > 5% are represented by
white rectangles, with the coded names of the species inside: Ariopsis felis (Af), Archosargus rhomboidalis (Ar), Bagre
marinus (Bm), Bairdiella chrysoura (Bc), Callinectes sapidus (Ca), Callinectes similis (Ci), Cathorops melanopus (Cm), Chaetodipteurs faber (Cf), Dasyatis sabina (Ds), Sphoeroides testudineus (St), Squilla empusa (Se), Stellifer lanceolatus (Sl) and
Xiphopenaeus kroyeri (Xk)
arise simply from strong similarities in assemblage
compositions. In other words, although species composition differs among local assemblages, the trophic
structure of nekton assemblages is rather similar.
In particular, the high species turnover holds also for
dominant species (Table 3). Indeed, 14 different species were ranked among the top 3 in terms of biomass
for more than 5% of the stations. Among these 14 species, 4 taxonomic groups of the 6 present at the
regional scale were represented (Teleostii and 3 crustacean families: Penaeidae, Squillidae and Portunidae). Therefore, we can reject the hypothesis that
our stable patterns at the local scale for trophic divergence and evenness result from the presence of a
couple of ubiquitous and abundant species that may
homogenize the trophic structure of nekton assemblages regardless of environment. For instance, the
most abundant species over all the samples, the dark
sea catfish Cathorops melanopus, was present in 60%
of the stations but was among the 3 most abundant
species in only 75% of these cases, or just 45% of the
312 stations. Conversely, the American stardrum
Stellifer lanceolatus was the most widespread species,
present in 71% of the stations, but among the 3 dominant species in only 28% of the cases. The Western
Atlantic seabream Archosargus rhomboidalis was
globally scarce but was quite abundant when present.
Moreover, because all abundant species, except the
Western Atlantic seabream and the mantis shrimp
Squilla empusa, have relatively similar trophic levels
(between 2.6 and 3.5); the biomass of nekton assemblages tended to peak at 3 in all the samples despite
high species turnover. As a consequence, trophic
evenness and trophic divergence indices, which
describe the distribution of biomass along the trophic
axis, tended to show stable patterns at the local scale
(Fig. 4) with weak relationships to environmental variables.
Species turnover is also remarkable for the 2 extremities of the trophic chain (Table 4). For the minimum
trophic level, 7 species (with trophic levels less than
2.7) contributed to 96% of the cases. As a result, in
64% of the stations studied, the minimum trophic level
was consistently low (less than 2.3). Similarly, 4 species
143
Villéger et al.: Stable trophic structure in nekton
Table 3. List of dominant species for trophic diversity patterns at the local scale (i.e. over the 312 sampling points). Main taxonomic groups are given in parentheses: Fi = Fish (Teleosteii), Cr = Crab (Portunidae), Sh = Shrimp (Penaeidae), and Msh = Mantis shrimp (Squillidae). The percentage of occurrence is the proportion of stations in which the species was present. The values
in the 4 last columns are the percentages of stations where the species was, respectively: the most abundant species (in terms of
biomass), the second-most abundant, the third-most abundant and, finally, where it was among the 3 dominant species
Taxon
Cathorops melanopus (Fi)
Callinectes sapidus (Cr)
Callinectes similis (Cr)
Xiphopenaeus kroyeri (Sh)
Stellifer lanceolatus (Fi)
Bairdiella chrysoura (Fi)
Archosargus rhomboidalis (Fi)
Bagre marinus (Fi)
Ariopsis felis (Fi)
Squilla empusa (Msh)
Sphoeroides testudineus (Fi)
Chaetodipterus faber (Fi)
Symphurus plagiusa (Fi)
Litopenaeus setiferus (Sh)
Trophic level
% occurrence
% 1st
% 2nd
% 3rd
% (1st, 2nd, or 3rd)
3.35
2.60
3.30
2.70
3.50
3.20
2.22
3.28
3.29
2.20
3.24
3.39
2.99
2.70
58.97
43.27
46.47
58.97
71.15
33.33
12.82
45.19
34.29
35.90
22.76
19.23
45.19
50.64
Total
28.85
13.14
9.94
9.62
6.41
4.81
3.53
2.88
2.56
2.24
2.24
1.92
1.60
0.32
90.06
9.94
6.73
6.41
8.01
12.82
4.17
3.85
5.13
5.45
4.17
4.49
2.24
2.56
2.88
78.85
6.09
7.69
7.05
8.33
9.62
2.24
2.88
3.85
5.45
2.56
3.85
2.24
4.49
2.56
68.900
44.88
27.56
23.40
25.96
28.85
11.22
10.26
11.86
13.46
8.97
10.58
6.40
8.65
5.76
contributed to 64% of the maximum trophic level
observed (16 for 90%), with trophic levels greater than
4. As a consequence, locally, the trophic range was
very consistent across space and time, ca. 1.7 to 1.8,
regardless of species composition and environmental
conditions. In addition, some species with low (the crab
Callinectes sapidus and the shrimp Xiphopenaeus
kroyeri) or high trophic levels (the fishes Cynoscion
arenarius and Cynoscion nothus) were ubiquitous
enough to prevent the decrease of trophic chain length
in all sampled assemblages. Indeed, these 4 species
did not have the most extreme TL values (the 2 low TL
values were ~2.6 and the 2 high TL values were ~4.1),
but when the species with the most extreme trophic
level values were absent, these 4 widespread species
prevented the trophic range from decreasing too much
and played an insurance role in maintaining the
trophic chain length. Thus, despite a high species
turnover among nekton assemblages, we observed a
stable trophic chain length attributable to a species
replacement for either low or high trophic values or to
the presence of some abundant and widespread species at the regional scale with both low and high
trophic levels.
Compared to the local scale, the patterns that
emerged at the regional scale were even more remarkable (Fig. 5). The histograms in Fig. 5 confirm the
regional predominance of species with trophic levels
~3.25: the class 3.25 to 3.5 consistently accounted for
more than half of the total nekton biomass. More precisely, for all the strata belonging to the wet and Nortes
seasons, the trophic class 3.25 to 3.5 accounted invariably for between 50 and 60% of the total biomass (in 5
Table 4. Species within extreme trophic levels as well as their
occurrences and their percentages of presence at the most
extreme trophic level in assemblages. Main taxonomic groups
are given in parentheses: Fi = Fish (Teleosteii), Cr = Crab
(Portunidae), Sh = Shrimp (Penaeidae), and Msh = Mantis
shrimp (Squillidae)
Taxon
Trophic % occur- % min
level
rence or % max
Minimum trophic levels
Squilla empusa (Msh)
Callinectes sapidus (Cr)
Cetengraulis edentulus (Fi)
Sicyonia brevirostris (Sh)
Archosargus rhomboidalis (Fi)
Litopenaeus setiferus (Sh)
Xiphopenaeus kroyeri (Sh)
Total
Maximum trophic levels
Cynoscion arenarius (Fi)
Trichiurus lepturus (Fi)
Cynoscion nothus (Fi)
Synodus foetens (Fi)
Total
2.2
2.6
2.11
2.2
2.22
2.7
2.7
35.90
43.27
17.31
12.82
12.82
50.64
58.97
26.60
18.27
17.31
10.58
9.62
9.62
3.85
95.85
4.28
4.45
4.04
4.5
37.18
19.87
27.56
8.97
24.68
18.91
10.58
8.97
63.14
cases out of 6, the value is very close to 60%), which is
not a trivial result. At this regional scale of observation,
the contribution of the most abundant species
Cathorops melanopus to the total biomass ranges from
14 to 46%. Therefore, the peak of biomass between
3.25 and 3.5 was not always and solely due to the dark
sea catfish but also to the swimming blue crab Callinectes similis (TL = 3.3) and to the hardhead sea catfish Ariopsis felis (TL = 3.29), with respective maxi-
144
Mar Ecol Prog Ser 364: 135–146, 2008
mum contributions of 20 and 8%. Moreover, besides
these dominant species, a lot of other species (8 to 19)
contributed to the peak between 3.25 and 3.5.
Overall, the results obtained at the regional scale
among the 9 spatiotemporal strata showed that variations in trophic diversity indices do not correlate with
the rate of species turnover in nekton assemblages. For
example, Nortes Coast 1 and Wet Coast 2 had a BrayCurtis index of 0.575, but exhibited low differences in
their trophic divergence and trophic evenness (Fig. 5).
In other words, 2 very different assemblages (in terms
of species composition and/or abundance) are as likely
to have similar patterns of trophic diversity as are 2
assemblages with similar specific composition. These
results suggest again that despite variations in species
composition and abundances among strata, we observed a remarkably stable pattern in the trophic
structure of nekton assemblages.
The composition of nektonic assemblages supports
the need for taking into account all organisms without
taxonomic considerations. Indeed, if we just consider
fish, we have a bias in the nekton trophic diversity estimation because shrimps and crabs are abundant in this
area and have low trophic levels compared to most fish
species. For example, among the 12 lowest trophic levels, only 5 corresponded to fish species; among these,
only 3 were abundant species. If shrimps and crabs
had not been captured or considered in the present
study, the base of the trophic chain would have been
partially reduced and our conclusions about stable
trophic structures would thus have been biased. For
example, the swimming blue crab Callinectes sapidus
and the shrimp Xiphopenaeus kroyeri constitute most
of the biomass for the trophic class between 2.5 and
2.75, which drives the trophic structure, especially for
the 2 coastal zones during the dry season (Fig. 5).
The sampling method using a shrimp trawl is probably the best for estimating nekton trophic diversity
because it is an active method and because the small
mesh size allows capturing both fish and small invertebrates.
A limitation in the present study comes from the estimation of the trophic level. Coastal areas and more
particularly coastal lagoons are marked by the presence of age classes from juveniles to adults, depending
on species’ reproductive strategies. Diet can change
with growth, and even if the corresponding trophic
levels are known, it is difficult to associate them correctly with each individual. Because the trophic level
increases with body size for high trophic level species,
we certainly overestimated the trophic level for small
individuals of species at high trophic levels (such as
Cynoscion sp.). Furthermore, in highly variable systems such as coastal zones, the diet of omnivorous
macroorganisms is not homogeneous throughout the
year, but follows migrations and seasons. In our data
set, some species have similar values for trophic level:
in total we had 101 species but only 66 different
trophic level values. Because trophic level is a continuous trait ranging from 2 to 4.5 for heterotrophic organisms, the number of entities would be a priori synonymous with species richness. However, estimated
trophic levels from the literature are sometimes provided at the level of the genus, with related species
then being assigned the same trophic level value. This
bias is not inherent in the trophic level concept but
instead arises from the lack of data, underlining the
difficulty of developing a large and complete database
of many species for functional traits. Finally, despite
these intrinsic and extrinsic biases due to the trophic
level estimation, we still consider that this ecological
trait has the advantage of providing a good approximation of the position of all nekton species along the
trophic chain without any taxonomic limitation.
Many studies dealing with trophic interactions in
aquatic systems elucidate 2 structuring forces (Leibold
et al. 1997, Maciej Gliwicz 2002). When the amount of
nutrient is the limiting factor, a bottom-up effect constrains the reproduction and growth of the lowest
trophic levels and then limits the abundance of their
predators (Ware & Thomson 2005, Frederiksen et al.
2006). The maximum biomasses are thus at the lowest
trophic levels. Conversely, when top predators are
abundant they may have a top-down effect on their
preys which themselves prey on species of lower
trophic levels. The biomasses of species belonging to
low trophic levels are under the control of the species
within the highest trophic levels. In the present case
study, none of these effects appeared to occur: we
observed stable trophic structures centered around the
middle of the trophic axis through space and time and
along environmental gradients. Indeed, on the one
hand, the 3 main streams carry a lot of organic matter
and nutrients throughout the year and especially during the wet season when the freshwater discharge is
high. This continuous flow may not limit the primary
productivity and primary consumption in the ecosystem and thus may prevent the trophic structure from
experiencing a bottom-up effect. On the other hand, as
described previously, top predators (i.e. species with
trophic levels higher than 4.0) are not as abundant in
biomass compared to the potential biomass of preys,
whatever the season or zone, and thus are unlikely to
provoke a top-down effect. Humans, through fishery
activities, may also modify trophic structure in species
assemblages (Pauly et al. 1998). The only fishery in the
Terminos Lagoon area is shrimp, primarily around the
city of Ciudad del Carmen. The nets used are like the
one we used for sampling. As explained previously,
these small mesh nets are nonselective, and there are
Villéger et al.: Stable trophic structure in nekton
high quantities of bycatch (crabs and fishes). Therefore, we suppose that fishing similarly decreases the
biomasses of all trophic levels and thus may not deeply
modify the trophic structure of nekton assemblages.
We have found advantages to describing trophic
diversity based on a set of indices that focuses on each
of its 3 independent components; indeed, our indices
summarized the shape of the Biomass Trophic Level
Spectra proposed by Sosa-López et al. (2005). Using
this promising approach, we have shown that the nekton assemblage trophic structure along the Terminos
coast is unexpectedly stable despite strong environmental gradients that enhance species turnover in
space and time. In turn, these results suggest that some
deterministic ecological processes may shape the
trophic structure of food webs, at least in nektonic
coastal assemblages. A comparison with similar systems is now needed to confirm the relative invariance
of trophic diversity along environmental gradients in
estuarine ecosystems. Our results also show that
researchers gain to consider all the organisms when
seeking stable trophic patterns in assemblages
because taxonomically different species may occupy
the same trophic level and have the same impact on
the food web. Such integrative approaches are implemented in Ecopath models to explore how biotic
groups transfer matter through the ecosystem (e.g.
Cruz-Escalona et al. 2007). The outputs of such models
would contribute to the understanding of mechanisms
underlying emergent properties of trophic structures.
Acknowledgements. We thank Luis Ayala Pérez and Francisco Gómez Criollo for their help during field work. This
research project was financed by the CONACYT and the government of the Campeche state (FOMIX 2005 CAM-C01-04).
This research was partly supported by a PICS programme
(MEXAPICS) financed by the CNRS. Three anonymous
reviewers provided very useful and constructive comments.
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Submitted: September 3, 2007; Accepted: February 22, 2008
Proofs received from author(s): July 9, 2008